The present invention relates generally to decoding information within communication systems and in particular, to a method for Multiple-Input-Multiple-Output (MIMO) detection and decoding within such communication systems.
Since wireless spectrum has become such a precious shared resource, higher spectral efficiency than state-of-the-art systems will likely be mandatory in future systems that are required to deliver higher data rates. It is well recognized that the Multiple-Input-Multiple-Output (MIMO) technique has the potential of greatly increasing spectral efficiency, but the gain comes at the price of more processing power at the receiver due to the task of separating signals that are simultaneously transmitted from multiple antennas. This fact is especially true for the optimal maximum likelihood (ML) receiver (which has the best performance among all receive methods) in that it has an exponential computational complexity of QMT for a modulation symbol constellation size of Q and MT transmit antennas. The optimal ML receiver needs to compute QMT distances between the actual received signal vector (formed from the received signals on all the receive antennas) and each of the QMT noise-free signal vectors that correspond to the QMT possible hypotheses for the transmitted symbol vector (i.e., a vector of constellation points). It is critical to reduce the complexity before an ML receiver can be used in systems using large Q and/or large MT.
Existing MIMO receive algorithms include non-linear ones like the above-mentioned ML receiver, and linear ones such as Minimum Mean Square Error (MMSE) and Zero Forcing (ZF) filters. While an ML receiver tries to jointly detect all MT transmitted symbols at once at the cost of QMT distance computations, a linear receiver first separates out the MT transmitted symbols and provides MT symbol estimates, based on which only QMT distance computations are needed. While linear techniques often require much less processing power than non-linear techniques, these techniques may not perform very well in some cases, such as when the noise power is high or when the channel matrix is not so well-conditioned (i.e., under poor channel conditions). On the other hand, while the ML receiver is generally more robust than linear receivers, the computational complexity of ML receivers may prohibit their implementation. Therefore, a need exists for a receiver that provides good performance in poor channel conditions yet has significantly less computational complexity than existing ML receivers.